Heart Disease Prediction Web Application

A machine learning-based web application that predicts the risk of heart disease based on various health parameters.

Live Demo

Access the Live Application Here

Features

• Interactive web interface for entering health parameters
• Real-time prediction of heart disease risk with confidence levels
• Visual risk assessment with intuitive color-coded indicators
• Detailed dashboard with data visualizations to understand risk factors
• Educational information about heart disease causes and prevention
• Mobile-responsive design works on all devices

Tech Stack

• Backend: FastAPI, Python
• Frontend: HTML, Tailwind CSS, JavaScript
• Data Processing: Pandas, NumPy
• Machine Learning: Scikit-learn (Logistic Regression)
• Visualization: Matplotlib, Seaborn, Chart.js

Installation

1. Clone this repository

   git clone https://github.com/yourusername/heart-disease.git
   cd heart-disease
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   uvicorn application:app --reload
   

4. Open your browser and navigate to:

   http://localhost:8000
   

How It Works

1. Input Collection: Users provide their health parameters through an intuitive form
2. Data Processing: The application processes and scales the input data
3. Prediction: A trained machine learning model predicts the likelihood of heart disease
4. Visualization: Results are displayed with intuitive visual representations
5. Risk Assessment: A detailed risk assessment is provided based on the prediction

Deployment

This application is successfully deployed on Render: https://heart-disease-2gln.onrender.com/

The application can also be deployed to other platforms:

• Render: Using the included render.yaml configuration
• Railway: Using the included Procfile
• Docker: Using the included Dockerfile

Model Details

The heart disease prediction model is trained using a dataset of patient health metrics. The model achieves high accuracy in identifying potential heart disease cases.

Key features used in prediction:

• Age and gender
• Chest pain type
• Resting blood pressure
• Serum cholesterol levels
• Fasting blood sugar
• Resting electrocardiographic results
• Maximum heart rate
• Exercise-induced angina
• ST depression induced by exercise
• Other cardiovascular indicators

Directory Structure

• application.py: Main FastAPI application
• Models/: Contains the trained machine learning model
• templates/: HTML templates for the web interface
• static/: Static assets (CSS, JavaScript, images)
• notebooks/: Jupyter notebooks used for model development and analysis

Future Improvements

• User accounts for tracking health metrics over time
• API integration for healthcare providers
• Enhanced data visualizations
• Integration with wearable device data

License

© 2025 Viraj Gavade. All Rights Reserved.

Connect with Me

• GitHub: github.com/yourusername
• LinkedIn: linkedin.com/in/viraj-gavade
• Twitter: @viraj_gavade
• Portfolio: virajgavade.com

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This project was developed as part of ML Learning Projects. If you have any questions or suggestions, feel free to reach out!